"""Load html from files, clean up, split, ingest into Weaviate."""
import os
from pathlib import Path
from markdown import markdown

import pickle
import re
from bs4 import BeautifulSoup
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from InstructorEmbedding import INSTRUCTOR

print(os.environ["HUGGINFACE_APIKEY"])

def clean_data(data):
    html = markdown(data)
    soup = BeautifulSoup(html, "html.parser")
    text = ''.join(soup.findAll(text=True))
    cleaned_text = re.sub(r"<!--.*?-->", "", text, flags=re.DOTALL)
    print(cleaned_text)
    return "\n".join([t for t in cleaned_text.split("\n") if t])

docs = []
metadatas = []
for p in Path("docs").rglob("*"):
    if p.is_dir():
        continue
    if str(p).lower().endswith(('.md', '.mdx')):
        with open(p) as f:
            filename = os.path.splitext(p)[0]
            docs.append(clean_data(f.read()))
            newfile_name = filename.replace("\\", "/")[5:]
            print("file:" + newfile_name)
            metadatas.append({"source": newfile_name})

text_splitter = CharacterTextSplitter(
    separator="\n",
    chunk_size=768,
    chunk_overlap=128,
    length_function=len,
)

documents = text_splitter.create_documents(docs, metadatas=metadatas)

print("making embedding")
model_name = "hkunlp/instructor-large"
embed_instruction = "Represent the text from the Hugging Face code documentation"
query_instruction = "Query the most relevant text from the Hugging Face code documentation"
embedding = HuggingFaceInstructEmbeddings(model_name=model_name, embed_instruction=embed_instruction, query_instruction=query_instruction)

print("beginning construction of faiss")
search_index = FAISS.from_documents(documents, embedding)

print("beginning pickle")
with open("docs.pkl", 'wb') as f:
    pickle.dump(search_index, f)

print("Pickle complete")